SUBNATIONAL AND NATIONAL LOYALTY: CROSS-NATIONAL COMPARISONS
Bibliographic record
Abstract
Is ethnic separatism the inevitable consequence of pursuing policies that allow for a reflowering of subnational ethnic identities, as in Quebec, or are there ways of having both a strong sense of attachment to one's own while still fostering loyalty to the larger state, as some variants of pluralist theory would have it? This is the central research question guiding our comparative study of the relationship between attachment to the individual ethnic group and loyalty to the larger country. Research on the relationship between strength of ethnic attachments and loyalty to the country as a whole impinges on the political wisdom of choosing public policies from affirmative action, to bilingual education to political autonomy for subregional groups. The comparative politics literature is fraught with assumptions about the nature of this relationship, but few studies have tried to empirically estimate it. Drawing on research by de la Garza et al. (1996) and Sidanius et al. (1997), we test pluralist, melting pot, and ethnic dominance models of ethnic attachment and overall levels of patriotism in the US and four other polyethnic states. Our data are derived from a 1995 ISSP National Identity Survey and the 1990–93 World Values Survey. We find mixed support for the alternative models when we replicate Sidanius and de la Garza and call for greater focus in cross-national surveys on assuring adequate samples of minority groups so that extant theories can be tested more fully.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".